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Description of archeological corrosion layers thanks to multivariate analysis

机译:多变量分析对考古腐蚀层的描述

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Predict how corrosion evolves for centuries in iron materials is decisive as well in nuclear field in order to evaluate the nuclear waste containers thickness as in cultural heritage for conservation of metal items. Our goal is to describe the Corrosion Product Layers (CPLs) which grew up under natural conditions in order to understand the corrosion mechanisms and the role of the layer in those phenomena. The aim of this study is to correlate both elementary and structural data thanks to chemometrics. The development of a reliable methodology is based on the case of archeological artefacts corroded in atmosphere. CPLs contain goethite, lepidocrocite, akaganeite and ferrihydrite (Fe_2O_3, nH_2O). Ferrihydrite is an electrochemical reactive phase which can see its reactivity decrease by adsorption of chemical species coming from the environment like phosphate and sulfate. Phases and chemical elements are heterogeneously distributed at micrometric scale and these phenomena have to be taken into account to describe the long term corrosion processes in atmosphere. In order to obtain a representative description on large areas, Raman and SEM-EDX hyperspectral images were acquired on the same zones. Hyperspectral images are about several tens of thousands spectra. Raman data have been analyzed by Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS). This algorithm allows to detect every crystalline phases in the analyzed zone and to quantify phase proportion. To evaluate the correlations between structural and chemical data, Raman and EDX hyperspectral images (micrometric resolution) have been examined together. A first approach consists in merging Raman and EDX spectra together after several preprocesses. Then a PCA is used on merged spectra. A second approach is based on the use of component images obtained from the analysis of both datasets separately. For Raman spectroscopy, the quantification images of component extracted by MCR-ALS are used. For EDX, intensity signals of each elements are extracted. Multiblock treatments are performed on the images obtained from these data to specify the possible correlation between elementary and structural information in CPLs. First results on merged spectra, thanks to PCA, seems to show a correlation between phosphorus and other minor elements as sulfur with ferrihydrite on one of the principal components.
机译:预测铁材料中腐蚀的演变历程也是决定性的,对于评估核废料容器的厚度(如在文化遗产中保护金属物品),核材料中的腐蚀也是决定性的。我们的目标是描述在自然条件下长大的腐蚀产物层(CPL),以便了解腐蚀机理以及该层在这些现象中的作用。这项研究的目的是通过化学计量学来关联基本数据和结构数据。一种可靠方法的发展是基于在大气中腐蚀的考古文物的情况。 CPL包含针铁矿,纤铁矿,赤铁矿和水铁矿(Fe_2O_3,nH_2O)。水铁矿是电化学反应相,通过吸附来自环境的化学物质(例如磷酸盐和硫酸盐),其反应性会降低。相和化学元素在微米尺度上是不均匀分布的,必须将这些现象考虑在内才能描述大气中的长期腐蚀过程。为了获得大面积的代表性描述,在相同区域上获取了拉曼和SEM-EDX高光谱图像。高光谱图像约为数万个光谱。拉曼数据已通过多元曲线分辨率交替最小二乘法(MCR-ALS)进行了分析。该算法可以检测分析区域中的每个结晶相,并量化相比例。为了评估结构数据和化学数据之间的相关性,拉曼和EDX高光谱图像(微米分辨率)已被一起检查。第一种方法是在几次预处理后将拉曼光谱和EDX光谱合并在一起。然后将PCA用于合并的光谱。第二种方法是基于使用分别从两个数据集的分析中获得的分量图像。对于拉曼光谱,使用了由MCR-ALS提取的组分的定量图像。对于EDX,提取每个元素的强度信号。对从这些数据获得的图像执行多块处理,以指定CPL中基本信息和结构信息之间的可能相关性。归功于PCA,合并光谱的初步结果似乎显示出磷与其他微量元素之间的相关性,例如硫与水铁矿是主要成分之一。

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